相关论文: Foreign-Object Detection in High-Voltage Transmiss…

The integrity of offshore wind turbines, pivotal for sustainable energy generation, is often compromised by surface material defects. Despite the availability of various detection techniques, limitations persist regarding…

Power lines are dangerous for low-flying aircraft, especially in low-visibility conditions. Thus, a vision-based system able to analyze the aircraft's surroundings and to provide the pilots with a "second pair of eyes" can contribute to…

This paper focuses on the key issue in autonomous driving: small target recognition in dynamic perception. Existing algorithms suffer from poor detection performance due to missing small target information, scale imbalance, and occlusion.…

AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection…

Object detection with Unmanned Aerial Vehicles (UAVs) has attracted much attention in the research field of computer vision. However, not easy to accurately detect objects with data obtained from UAVs, which capture images from very high…

Small object detection has been a challenging problem in the field of object detection. There has been some works that proposes improvements for this task, such as adding several attention blocks or changing the whole structure of feature…

Underwater object detection constitutes a pivotal endeavor within the realms of marine surveillance and autonomous underwater systems; however, it presents significant challenges due to pronounced visual impairments arising from phenomena…

Although convolutional neural networks have made outstanding achievements in visible light target detection, there are still many challenges in infrared small object detection because of the low signal-to-noise ratio, incomplete object…

As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution…

With the high density of printed circuit board (PCB) design and the high speed of production, the traditional PCB defect detection model is difficult to take into account the accuracy and computational cost, and cannot meet the requirements…

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in…

This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements consist of the EfficientRep Backbone for robust feature…

Enhancing the network architecture of the YOLO framework has been crucial for a long time, but has focused on CNN-based improvements despite the proven superiority of attention mechanisms in modeling capabilities. This is because…

The development of autonomous driving technology must be inseparable from pedestrian detection. Because of the fast speed of the vehicle, the accuracy and real-time performance of the pedestrian detection algorithm are very important. YOLO,…

Object detection is vital in precision agriculture for plant monitoring, disease detection, and yield estimation. However, models like YOLO struggle with occlusions, irregular structures, and background noise, reducing detection accuracy.…

Small object detection via UAV (Unmanned Aerial Vehicle) images captured from drones and radar is a complex task with several formidable challenges. This domain encompasses numerous complexities that impede the accurate detection and…

Small object detection remains a challenging problem in the field of object detection. To address this challenge, we propose an enhanced YOLOv8-based model, SOD-YOLO. This model integrates an ASF mechanism in the neck to enhance multi-scale…

The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOLO11 and earlier versions and the area-based self-attention…

Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of…

This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. Key innovations, including the CSPNet…